Advances in computing hardware and software have fueled exponential growth in the generation of vast amounts of data due to increased computations and analyses in numerous areas, such as in the various scientific and engineering disciplines, as well as in the application of data science techniques to endeavors of good-will (e.g., areas of humanitarian, environmental, medical, social, etc.). Also, advances in conventional data storage technologies provide the ability to store the increasing amounts of generated data. Consequently, traditional data storage and computing technologies have given rise to a phenomenon in which numerous disparate datasets have reached sizes and complexities that traditional data-accessing and analytic techniques are generally not well-suited for assessing conventional datasets.
Conventional technologies for implementing datasets typically rely on different computing platforms and systems, different database technologies, and different data formats, such as CSV, TSV, HTML, JSON, XML, etc. Further, known data-distributing technologies are not well-suited to enable interoperability among datasets. Thus, many typical datasets are warehoused in conventional data stores, which are generally “data silos,” whereby data in the associated data stores are often difficult to connect to other sources of data. These data silos have inherent barriers that insulate and isolate datasets. Further, conventional data systems and dataset accessing techniques are generally incompatible or inadequate to facilitate data interoperability among the data silos.
Conventional approaches to provide dataset generation and management, while functional, suffer a number of other drawbacks. For example, disparate approaches to gathering, forming, and analyzing datasets typically require different, ad hoc approaches. For example, data scientists and other consumers of data generally undertake significant effort during a variety of steps in which a dataset is downloaded and analyzed. In particular, data practitioners usually perform personalized queries and data analyses, manually, on the downloaded dataset to determine whether the downloaded dataset is of any use. Contextual information for understanding the downloaded dataset is usually absent, due to the ad hoc nature of dataset development, thereby complicating the process by which data practitioners assess the worthiness of a dataset. Further, differently-formatted repositories of data provide further challenges when assessing multiple dataset with multiple versions of ad hoc queries. Hence, these approaches are not typically well-suited to resolve sufficiently the drawbacks of traditional techniques of dataset generation and analysis. Moreover, traditional dataset generation and management are not well-suited to reducing efforts by data scientists and data practitioners in extracting, transforming, and loading data into data stores in a manner that serves their desired objectives.
Thus, what is needed is a solution for facilitating techniques to discover, form, and analyze datasets, without the limitations of conventional techniques.